Multisensor data available from airborne and spaceborne platforms have become widely used to study the changes of land, oceanic, atmospheric and ionospheric parameters, and their relation to various natural hazards. For example, significant changes prior to earthquake events have been observed in Surface Latent Heat Flux (SLHF), Sea Surface Temperature (SST), water vapor and chlorophyll concentration. Suggesting the presence of some interaction between the lithosphere and atmosphere, these observed changes have created an interest in using satellite-based observations to identify and study earthquake precursors.
Routine SLHF measurements can provide early warnings of an impending earthquake. With respect to coastal earthquakes, anomalous SLHF peaks appear to consistently occur a few days prior to the main earthquake event. The magnitude of each peak tends to vary, while SLHF tends to be higher over oceans and lower over land. The origin of anomalous SLHF peaks is likely to be related with the manifestations of the surface temperature in the epicentral region, which can be associated with the building up of stress and movement along faults.
It is believed that temperature increases prior to an earthquake. To show changes in temperature, infrared (IR) wavelengths from a Moderate Resolution Imaging Spectroradiometer (MODIS) sensor may be used. There may be various explanations, such as friction along a fault or fluid movement, as to why the temperature may rise. In addition, SST may also increase due to heat conduction. A rise in SST may cause ocean evaporation to increase, which in turn may raise anomalous SLHF peaks prior to a main earthquake.
Annually, SLHF contains a large number of maxima peaks, several of which are more than 1 or 2 times above the standard deviation. These peaks can be attributed to atmospheric phenomena, earthquakes or ocean disturbances. Problematically, it is difficult to identify the maximum SLHF peak as a precursor of an impending earthquake. The detection of the maximum SLHF peak is significant to alert and allow affected regions to prepare for an impending earthquake. For example, had the maximum SLHF peak of the earthquake that struck the Indian Ocean in Dec. 26, 2004 and caused the great tsunami disaster thereafter been detected, hundreds of thousands of people could have been forewarned and prepared for evacuation. Hence, what is needed is a general methodology and model to employ spatial and/or temporal analysis of wavelet maxima to identify signals associated with earthquakes with precise continuity in time and space.
Several commercial and research models have been developed for an early warning system that mainly uses past historical data, some of which can be traced back to as far as the 5th century BC. For example, one model uses past historical data that includes fracture zones calculated using gravity fields, and changes in the electromagnetic field and tidal cycles to determine the occurrence of earthquakes. It further uses ground based data. However, only one ground monitoring station is available. Thus, it precludes the possibility of real-time prediction. Therefore, what is also needed is a general methodology and device that identify signals associated with earthquakes in real-time.